Meshes are frequently applied or mapped to medical images to segment one or more anatomical structures contained therein. Here, the term ‘segmentation’ refers to the identification of an anatomical structure in the medical image, e.g., by delineation of the boundaries of the anatomical structure, by labeling of the voxels enclosed by the boundaries, etc. Once such segmentation has been performed, it is possible to extract clinical parameters such as, in case of, e.g., a cardiac structure, ventricular volume and wall thickness. Such segmentation is frequently also referred to as delineation or annotation.
An applied mesh or mapped mesh may also be displayed together with the medical image to visualize information, such as the shape and location of the anatomical structure. For example, a mesh may be shown as an overlay to a three-dimensional (3D) medical image such as a Computed Tomography (CT), Magnetic Resonance Imaging (MRI) or Ultrasound (US) image. Such a 3D image may, together with the applied mesh, be visualized in various ways. Therefore, alternative terms for applied mesh may be mapped mesh, fitted mesh, overlaied mesh, or superimposed mesh. Examples of using the applied mesh or mapped mesh for segmenting a three-dimensional (3D) medical image can be found in WO2004047030A2. For example, the 3D image may be visualized by multi-planar reformatting to generate a 2D view intersecting the 3D image and the applied mesh or mapped mesh. In such a 2D view, the mesh may be shown as a contour. Other view generation techniques are known within the field of 3D image visualization, and include volume rendering and maximum intensity projection.
Meshes may be applied or mapped automatically, manually or semi-automatically to a medical image. The automatic or semi-automatic application may involve the use of an adaptation technique, also termed ‘mesh adaptation’ or ‘mesh fitting’. The adaptation technique may, for example, optimize an energy function based on an external energy term which adapts the mesh to the image data and an internal energy term which maintains a rigidness of the mesh. Various adaptation techniques are known for automatically applying meshes to medical images. An example of an automatic technique is described in “Automatic Model-based Segmentation of the Heart in CT Images” by O. Ecabert et al., IEEE Transactions on Medical Imaging 2008, 27(9), pp. 1189-1201, which describes the automatic segmentation of the heart from three-dimensional (3D) Computed Tomography (CT) images.
A previously applied mesh or mapped mesh may require editing by a user. For example, the mesh may be insufficiently accurately applied to serve for diagnostic purposes, generation of normative databases, or generation of ground truth data as input for machine learning algorithms. Such mesh editing may be performed interactively using a view of the 3D image and the applied mesh or mapped mesh which shows a part of the mesh which is to be edited. Such editing may involve, e.g., re-positioning of a mesh part, increasing the resolution of a mesh part, etc.
Disadvantageously, the orientation of the view in the 3D image may be ill-suited for the planned edition action. In conventional image viewing applications, the view orientation may be switched between coronal, sagittal, and transversal orientations. However, this may provide insufficient flexibility for obtaining a suitable view. In some applications, it is possible to interactively reformat the view to an arbitrary orientation, but adapting it on-the-fly to the optimal orientation during mesh editing is prohibitively time-consuming.